Ion trap and release dynamics enables nonintrusive tactile augmentation in monolithic sensory neuron

An iontronic-based artificial tactile nerve is a promising technology for emulating the tactile recognition and learning of human skin with low power consumption. However, its weak tactile memory and complex integration structure remain challenging. We present an ion trap and release dynamics (iTRD)–driven, neuro-inspired monolithic artificial tactile neuron (NeuroMAT) that can achieve tactile perception and memory consolidation in a single device. Through the tactile-driven release of ions initially trapped within iTRD-iongel, NeuroMAT only generates nonintrusive synaptic memory signals when mechanical stress is applied under voltage stimulation. The induced tactile memory is augmented by auxiliary voltage pulses independent of tactile sensing signals. We integrate NeuroMAT with an anthropomorphic robotic hand system to imitate memory-based human motion; the robust tactile memory of NeuroMAT enables the hand to consistently perform reliable gripping motion.

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Supplementary Text Figs. S1 to S32 Table S1 Legends for movies S1 and S2 References
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Supplementary Text Theoretical background of ion dynamics
For a solution electrolyte, the ion flux (  ) can be mathematically described by considering contributions from both ion diffusion and ion migration, and is given by the following equation; The first term (  ∇  ) represents the contribution from the ion diffusion, where   is the diffusion constant.The second term (       ∇ ) corresponds to the contribution of ion migration, representing the movement of ions with an ionic charge (  ) and mobility (  ).Thus, assuming a chemical equilibrium state (∇  = 0), the early stage of the ion flux is primarily determined by the interplay between ion concentration (   ) and the electric potential gradient ( ∇ ) which are correlated with the ion migration.By employing Einstein's equation (   =      ⁄ ), and substituting   into the ion flux equation, we derived the Nernst-Plank equation as follows; where  is the electronic charge,   is the Boltzmann constant and  is temperature.The molecular theories of ion dynamics and transport are primarily based on Poisson-Nernst-Plank (PNP) equation.From a Nernst-Plank equation, a potential distribution in an electrolyte can be determined by the Poisson equation as follows; where  is the dielectric constant of the medium and  is the net charge density.For a symmetrical electrolyte and assuming, we can develop Debye-Falkenhagen equation in terms of the potential as follows.
where  −1 is the Debye screening length which is given by where  is the ionic strength,   is the Boltzmann's constant,   is the Avogadro's number,   is the dielectric constant,  0 is the permittivity of free space and  is the temperature.From these equations, we can deduce that an ionic concentration is the main factor contributing to a variation of the potential distribution and the Debye screening length (corresponding to a thickness of diffuse layer in the electrolyte).For a sinusoidal applied potential ( = ()  ), the Debye-Falkenhagen equation is described as follows (27); where   = 1  2  ⁄ is the Debye time and  is the frequency.We note that observation of the Debye time provides valuable insights to comprehend ion trap and release dynamics (iTRD) because the Debye time depends on an ionic concentration regardless of a dimensional change (e.g., thickness and area) of iTRD-iongel film.
A bode plot is a frequency-dependent impedance spectrum, by which dielectric relaxation times of charged species in iongel materials can be investigated empirically.As shown in Fig. 2H and fig.S8B, Debye relaxation frequency (ω  =   −1 ) can be extracted from an intersection point of real part (Z′) and imaginary part (Z″) impedance spectra in Bode plots (45).According to the Debye-Falkenhagen relation (Eq.S6), a shift of Debye relaxation frequency toward higher frequencies means a shorter Debye screening length caused by an increase of ionic concentration in iongel materials.Based on this theoretical interpretation, the higher frequency shift of the Debye relaxation frequency (decreased Debye time) under pressure can be regarded as an increase of ionic concentration in the iTRD-iongel (Fig. 2H), which was attributed to the release of trapped ions.2B, respectively.The detailed calculation method was followed by the previous studies (29,30).

Fig. S1 .
Fig. S1.Pair correlation function (PCF) of hydrogen on the top of silica surface and the components of (A) [TFSI] -and (B) [EMIM] + , respectively.The ρg(r)a-b of each atomic pairs was utilized for direct comparison of atomic distribution, which is determined by calculating the probability of finding 'a' atom around 'b' atom separated by interatomic distance over the equilibrium structure (a: Hydrogen atom of silica, b: Carbon, hydrogen, fluorine, nitrogen, and oxygen atoms of [EMIM] + and [TFSI] -).

Fig. S5 .
Fig. S5.Finite element method (FEM) calculation of the pristine iongel.The effective stress and strain within the pristine iongel were exhibited when pressure was applied.

Fig. S6 .
Fig. S6.Binding energy curves as a function of distance between silica surface and [cisoid-TFSI] by DFT calculations.

Fig. S13 .
Fig. S13.Transfer characteristics of pristine iongel-based synaptic transistor and iTRDdriven NeuroMAT.The electrical properties were evaluated depending on the applied mechanical stimuli from 0 kPa to 100 kPa.

Fig. S19 .
Fig. S19.(A and B) Phase angle and capacitance plots as a function of frequency for a capacitor with PDTTPP/iTRD-iongel.The phase behavior of the impedance spectra was analyzed by applying the different DC voltage with a rms amplitude of 10 mV under pressure.The capacitance was extracted from the complex impedance in an equivalent circuit which consists of a resistor and a capacitor in parallel.

Fig. S20 .
Fig. S20.EPSC decay characteristics of iTRD-driven NeuroMAT depending on the number of VG pulses.The EPSCs were fitted (red lines) and the two decay times (  and   ) were extracted by bi-exponential decay model (  =   +    −/  +    −/  ,   and   are corresponding to depolarization of EDL and anion de-doping time, respectively) (31).

Fig. S25 .
Fig. S25.Energy consumption of NeuroMAT depending on pressure under continuous VG stimuli.The calculated energy consumption of NeuroMAT was extracted from Fig. 3B.

Fig. S29 .
Fig. S29.Photograph of bending motion of the robotic hand.The bending angle was determined by the width of pulse output.

Fig. S30 .
Fig. S30.Long-term tactile memory characteristics of iTRD-driven NeuroMAT.The tactile memory induced by a single-trial learning was maintained over 7500 s.